Reconfigurable Intelligent Surfaces (RIS) have emerged as a key architectural element in 6G wireless systems, enabling programmable control over electromagnetic (EM) propagation. However, prevailing RIS models are limited in two critical ways: (i) they assume deterministic or Gaussian signal environments, neglecting the inherent spatial randomness of RIS deployments and user locations; and (ii) they treat EM signals as scalars, overlooking the intrinsic multigrade (e.g., scalar, vector, bivector) structure of physical fields. In this paper, we introduce CRISP, a novel Clifford Wiener–Itô Poisson chaos framework for RIS modeling under spatial randomness. By combining stochastic geometry (via Poisson point process), multivector Clifford algebra, and Wiener–Itô chaos expansions, we develop a grade-separated stochastic representation of RIS channels. This formulation enables optimal RIS beamforming via per-grade chaos projection, yielding closed-form expressions for grade-wise residual power. Extensive simulations show that CRISP achieves 10–15 dB lower residual power compared to scalar RIS baselines under Poisson-deployed scattering. Our framework provides a new mathematical foundation for RIS optimization in random and semantic wireless environments, and opens new directions in multigrade-aware, chaos-driven RIS control for 6G and beyond.

This article introduces uncrewed aerial vehicles (UAVs) assisted communication system for energy-constrained Internet of Things (IoT) devices. Herein, a power beacon transmits radio frequency (RF) signals to enable energy harvesting at the IoT devices (IoDs). The source IoD utilizes harvested energy to transmit information to the destination IoD through multiple UAV relays. Among multiple UAV relays, a selected UAV applies a decode-and-forward operation to relay the information to the destination. An eavesdropper near the destination IoD poses a threat to information secrecy. Consequently, the selected UAV injects artificial noise to degrade the accessibility of the eavesdropper’s link. For this setup, we employ a composite fading channel model by combining log-normal distribution for shadowing effects and Nakagami-m distribution for small-scale fading to evaluate system performance. Specifically, the compact expressions for secrecy outage probability and effective secrecy throughput (EST) are derived using the Gauss-Hermite quadrature method. Moreover, we conduct a tradeoff analysis between reliability and security by factoring in outage and intercept probabilities. Lastly, we formulate an EST maximization problem by optimizing the system parameters and UAV’s locations under the outage and secrecy constraints. We propose a particle swarm optimization and continuous genetic algorithm-based hybrid algorithm for solving the nonlinear optimization problem. The numerical findings demonstrate that the proposed algorithm outperforms the baseline algorithms regarding the achieved EST.

Multicasting is a vital information dissemination technique in Software-Defined Networking (SDN). With SDN, a multicast service can incorporate network functions implemented at different nodes, which is referred to as software-defined multicast. Emerging ubiquitous wireless networks for 5G and Beyond (B5G) inherently support multicast. However, the broadcast nature of wireless channels, especially in dense deployments, leads to neighborhood interference as a primary system degradation factor, which introduces a new challenge for software-defined multicast in wireless mesh networks. To tackle this, this paper introduces an innovative approach, based on the idea of minimizing both the total length cost of the multicast tree and the interference at the same time. Accordingly, a novel bicriteria optimization problem is formulated–Minimum Interference Steiner Tree (MIST), which is the edge-weighted variant of the vertex-weighted secluded Steiner tree problem [1]. To solve the bicriteria problem, instead of resorting to heuristics, this paper employs an innovative approach that is an approximate algorithm for MIST but with guaranteed performance. Specifically, the approach exploits the monotone submodularity property of the interference metric and identifies Pareto optimal solutions for MIST, then converts the problem into the submodular minimization under Steiner tree constraints, and designs a two-stage relaxation algorithm. Simulation results demonstrate and validate the performance of the proposed algorithm.

This paper presents a study on peak age of information (AoI), focusing on its distribution that is more important for AoI guarantees than the mean. Specifically, the relation of peak AoI to the underlying information generation and transmission processes is explicitly formulated. Based on this formulation and by exploring the independence information between the information generation and transmission processes, a general bound on the distribution of peak AoI is derived. To showcase the use of the derived bound, it is applied to two representative cases, which are characterized by the M/M/1 and D/M/1 queuing models. Numerical results obtained from the proposed bound analysis are finally introduced and discussed in comparison with exact results, validating the bound.

Vehicular communications offer the ability to vehicles to communicate with other vehicles and the network infrastructures so that the information transfer can help in hassle-free and safe transportation. In particular, vehicle-to-network (V2N) communications can be realized directly or using an intermediary/relay node in the form of roadside infrastructure, depending on the distance between the vehicle and network infrastructure. In this paper, we propose a system to establish communication between vehicles and network (base station) with the help of roadside infrastructure, e.g., traffic light as an intermediary node, in two phases. In the first phase, information transfer between vehicle-to-infrastructure (V2I) uses the radio-frequency (RF) channel. In the second phase, communication between infrastructure and network is accomplished using hybrid free-space optics (FSO)/RF channel. We first derive the closed-form expression for the system outage probability for this system setup. Then, we formulate the expression for system throughput considering a delay-limited scenario. We validate the accuracy of analytical results with the simulations.

Bitcoin is the first and the most extensive decentralized electronic cryptocurrency system that uses blockchain technology. It uses a peer-to-peer (P2P) network to operate without a central authority and propagate system information such as transactions or blockchain updates. The communication between participating nodes is highly relying on the underlying network infrastructure to facilitate a platform. Understanding the impact of peer formation strategies, peer list, and delay is vital in understanding node to node communication and the system performance. Therefore, we performed an extensive study on the transaction characteristics of Bitcoin through a testbed. The analysis shows that peer selection strategies affect the transaction propagation and confirmation times. In particular, better performance, in terms of smaller transaction confirmation time and lower number of temporary forks, may be achieved by adjusting the default nearby-based peer selection strategy.

We explore the use of software-defined networking (SDN) technology in building a communication network for smart grid. With cyber-physical interdependence, such communication network may suffer from cross-network cascading failures. To prevent the failures, we perform virtual network function (VNF) orchestration jointly with power-disjoint routing. Our work is novel in proposing an efficient scheme to find power-disjoint routes at the same time of performing VNF orchestration. We formulate an optimization to maximize the ratio of power-disjoint route count to VNF orchestration cost. The optimization has a non-linear non-convex objective function. We propose a two-level hierarchical solution approach. At higher level, the scheme converts the problem into a fractional maximum flow circulation, which can be solved using simplex method to find the maximum number of power-disjoint routes. Given a higher level solution, the lower level aims to minimize the VNF orchestration cost while satisfying VNF chaining and placement requirements. This lower level hierarchy uses the Dijkstra’s algorithm in building a sequence of minimum spanning trees, each roots at the current VNF hosting node in a VNF chain. Extensive simulation results confirm that the proposed scheme can find the maximum number of power-disjoint routes and minimize the cost within a second, for a system with 120 communication nodes. The results show that the number of power-disjoint routes can be increased by increasing either the number of nodes or node degree, but only the node degree can keep the cost flat. Therefore, one should build a robust software-defined smart grid communication network by enhancing node connectivity.

An appealing feature of Network Function Virtualization (NFV) is that in an NFV-based network, a network function (NF) instance may be placed at any node. On the one hand this offers great flexibility in allocation of redundant instances, but on the other hand it makes the allocation a unique and difficult challenge. One particular concern is that there is inherent correlation among nodes due to the structure of the network, thus requiring special care in this allocation. To this aim, our novel approach, calledCoShare, is proposed. Firstly, its design takes into consideration the effect of network structural dependency, which might result in the unavailability of nodes of a network after failure of a node. Secondly, to efficiently make use of resources, CoShare proposes the idea ofshared reservation, where multiple flows may be allowed to share the same reserved backup capacity at an NF instance. Furthermore, CoShare factors in the heterogeneity in nodes, NF instances and availability requirements of flows in the design. The results from a number of experiments conducted using realistic network topologies show that the integration of structural dependency allows meeting availability requirements for more flows compared to a baseline approach. Specifically, CoShare is able to meet diverse availability requirements in a resource-efficient manner, requiring, e.g., up to 85% in some studied cases, less resource overbuild than the baseline approach that uses the idea ofdedicated reservationcommonly adopted for redundancy allocation in NFV.

Network Function Virtualization enables network operators to modernize their networks with greater elasticity, network programmability, and scalability. Exploiting these advantages requires new and specialized designs for management, automation, and orchestration systems which are capable of reliably operating and handling new elements such as virtual functions, virtualized infrastructures, and a whole new set of relationships among them. Operations such as resource allocation, instantiation, monitoring, scaling, or termination of virtual functions are key lifecycle operations that NFV management and orchestration (NFV-MANO) frameworks need to correctly perform. Failures of the NFV-MANO prevent the network ability to respond to new service requests or events related to the normal lifecycle operation of network services. Thus, it is important to ensure robustness and high availability of the MANO framework. This paper adopts a model-driven approach to predict the availability of the NFV-MANO and assess the impact that different failure modes have. We propose different models, based on Stochastic Activity Networks (SANs), which abstract various MANO deployment configurations, inspired by current containerized open-source MANO implementations. Moreover, we integrate software rejuvenation and investigate the trade-off between its associated overhead and system availability increase. An extensive experimental campaign with fault-injection techniques on a real-life MANO implementation allows to derive a number of realistic recovery parameters. The case studies are used to quantitatively evaluate the steady-state availability and identify the most important parameters influencing system availability for the different deployment configurations.

The fifth generation (5G) mobile networks are envisioned to provide connectivity not only to mobile users but also to a wide range of other services such as enhanced mobile broadband (eMBB) and massive Internet of Things (mIoT). In order to meet the diverse requirements of these services in 5G, Software Defined Networking (SDN) has been proposed as an enabling technology for both the core cloud and the edge cloud, in addition to Network Slicing to achieve isolation among services. In this paper, an analytical model is developed for such an SDN-based edge cloud, focusing on the support of two services: eMBB and mIoT. To illustrate the use of the model, delay analysis of a switching node in the edge cloud is presented. The results show the relation between the packet delay and the underlying system parameters, such as slice density, and the impact of the SDN controller on the delay. An implication of the model, analysis and results is that they may be used for network / resource planning and admission control in 5G edge clouds to meet delay requirements of the services.

By forming a temporary group, users in mobile social networks (MSNs) can disseminate data to others in proximity with short-range communication technologies. However, due to user mobility, airtime available for users in the same group to disseminate data is limited. In addition, for practical consideration, a star network topology among users in the group is expected. For the former, unfair airtime allocation among the users will undermine their willingness to participate in MSNs. For the latter, a group head is required to connect other users. These two problems have to be properly addressed to enable real implementation and adoption of MSNs. To this aim, we propose a joint head selection and airtime allocation scheme for data dissemination within the group using Nash bargaining theory. Specifically, we consider two cases in terms of user preference on the data to be disseminated: a homogeneous case and a heterogeneous case. For each case, a Nash bargaining solution (NBS) based optimization problem is proposed. The existence of optimal solutions to the optimization problems is proved, which guarantees Pareto optimality and proportional fairness. Next, an algorithm that allows distributed implementation is introduced. Finally, numerical results are presented to evaluate the performance, validate intuitions and derive insights of the proposed scheme.

Currently, cloud computing is a primary enabler of new paradigms such as edge and fog computing. One open issue is the pricing of services or resources. Current pricing schemes are usually oligopolistic and not fair. In this work, we propose OFM, an online learning based marketplace that dynamically determines the price for arbitrary resource types based on supply and demand existing at that period. Unlike state of the art solutions, OFM can handle an arbitrary number of customers and resource types at every instance of time. It further performs integral allocation of resources and thereby avoids the unbounded integrality gap. We evaluate OFM with both real and synthetic datasets to reflect varying buying interests, the number of resources sold and market volatility to demonstrate the feasibility of our solution for several realistic scenarios. We observe that (i) OFM achieves about 9% of optimal prices and maximizes the Nash social welfare (NSW); (ii) OFM converges faster and works with different data distributions; and (iii) OFM scales for a large number of resources and buyers and computational time is in the order of microseconds, making it applicable for real-time use cases especially in edge markets.

We propose an approach to calculate delay bound for multiprocessor real-time systems scheduled by GEDF. Different from most existing analysis techniques analyzing sporadic tasks, we consider bursty tasks which have more general arrival patterns. In detail, we use shapers to eliminate burst in original system inputs and generate sporadic job sequences, and then calculate the delay bound of each task. To further improve our approach, we design a heuristic algorithm to make as more tasks as possible to meet their deadlines by adjusting settings of shapers. Experiments show that the proposed algorithm can lead to improvement of acceptance ratio and the delay bound derived is much smaller than that by compared existing work.

WiFi is the dominant means for home Internet access, yet is frequently a performance bottleneck. Without reliable, satisfactory performance at the last hop, end-to-end quality of service (QoS) efforts will fail. Three major reasons for WiFi bottlenecking performance are its: 1) inherent wireless channel characteristics, 2) approach to access control of the shared broadcast channel, and 3) impact on transport layer protocols, such as TCP, that operate end-to-end, and over-react to the loss or delay caused by the single WiFi link. In this paper, we leverage the philosophy of centralization in modern networking and present our cross layer design to address the problem. Specifically, we introduce centralized control at the point of entry/egress into the WiFi network. Based on network conditions measured from buffer sizes, airtime and throughput, flows are scheduled to the optimal utility. Unlike most existing WiFi QoS approaches, our design only relies on transparent modifications, requiring no changes to the network (including link layer) protocols, applications, or user intervention. Through extensive experimental investigation, we show that our design significantly enhances the reliability and predictability of WiFi performance, providing a “virtual wire”-like link to the targeted application.

Network Function Virtualization (NFV) has recently attracted telecom operators to migrate network functionalities from expensive bespoke hardware systems to virtualized IT infrastructures where they are deployed as software components. Scalability, up-gradation, fault tolerance and simplified testing are important challenges in the field of NFV. In order to overcome these challenges, there is significant interest from research communities to scale or decompose network functions using the monolithic and microservice approach. In this paper, we compare the performance of both approaches using an analytic model and implementing test-bed experiments. In addition, we calculate the number of instances of monoliths or microservices in which a network function could be scaled or decomposed in order to get the maximum or required performance. Single and multiple CPU core scenarios are considered. Experimentation is performed by using an open source network function, SNORT and running monoliths and microservices of SNORT as Docker containers on bare metal machines. The experimental results compare the performance of monolith and microservice approaches and are used to estimate the validity of the analytic model. The results also show the effectiveness of our approach in finding the number of instances (monoliths or microservices) required to maximize performance.

Real-Time Calculus (RTC) is a powerful framework for modeling and analyzing complex networked real-time systems. RTC builds up on and shares many similarities with Network Calculus (NC), but some concepts are not completely the same in RTC and NC. One of the most important properties in NC is pay-burst-only-once, which can improve the precision of end-to-end performance analysis. Naturally, people would expect the pay-burst-only-once property to also hold in RTC. In fact, some existing work has used it in some performance analysis problems. Unfortunately, the pay-burst-only-once property has never been proved in RTC. There are even some results seeming to be against the pay-burst-only-once property in RTC. In this paper, we prove that the pay-burst-only-once property indeed holds in RTC.

WiFi Direct is an embedded technology in a vast majority of smartphone devices running the Android operating system. As a result, it represents a promising technology that can be exploited in re-establishing connectivity among user devices in case of cellular network outages. A technique that smart devices can use to restore connectivity in situations where they are unable to connect to a cellular tower or access point, but close enough to support device-to-device communication is presented. The proposed technique envisions a combination of security layers that ensure authentication, confidentiality, and integrity of communications among end users. Each device is issued a certificate by a central authentication entity at sign up and when it is unable to connect to the server component, it will attempt to form a group with nearby devices in the same situation over WiFi Direct. Once a WiFi Direct group has been formed, the group owner will temporarily assume the role of the server, and each group member and the group owner will verify each others identity and connect using mutual Transport Layer Security (mTLS), facilitating secure communication. The approach is validated through the implementation of a mobile social application involving several mobile devices, and overheads due to the additional security features are investigated.

In this paper, the buffer-constrained throughput performance of a multi-user wireless-powered communication (WPC) system is investigated, where energy harvesting follows a non-linear model. The investigation focuses on the buffer overflow performance of sending data in the downlink (DL) from the access point (AP) node to each user equipment (UE) node and that in the uplink from each UE node to the AP node, based on which the throughput performance on both directions when a buffer constraint is enforced is studied. Specifically, the buffer overflow probability at each node is analyzed, based on which the buffer-constrained throughput is studied. In addition, to ensure the throughput performance under the buffer constraint, the DL transmission power allocation policy and the required energy storage capacity at each UE are investigated. Also, the optimal channel time allocation policy is studied with the objective of maximizing the minimum buffer-constrained throughput guaranteed to each UE at the same time. To this aim, an optimization problem is first formulated and then a dichotomy-based time allocation algorithm combined with a one-dimensional search is proposed to solve this problem. The analysis and results, explicitly relating the throughput to the buffer constraint in addition to WPC characteristics, shed new light on the design and performance analysis of WPC systems.

The net neutrality principle, also known as Open Internet, states that users should have equal access to all Internet content and that Internet Service Providers (ISPs) should not practice differentiated treatment on any of the Internet traffic. While net neutrality aims to restrain any kind of discrimination, it also provides exemption for a certain category of Internet traffic known as specialized services (SS), by allowing the ISP to dedicate part of the resources for the latter. In this work, we shed light on this particular case by comparing five Radio Access Technology (RAT) selection policies in heterogeneous wireless networks where SS traffic and Internet Access Services (IAS) traffic are carried. The studied policies include a non-net-neutral revenue-maximizing policy used as reference policy, and four other net-neutral policies with and without exemption to SS traffic. The results show that, even though, as expected, integrating net neutrality regulation within RAT selection policies can lead to a decrease in the generated revenue, a properly designed net-neutral policy will not only be able to reduce this decrease in revenue but also can maintain a similar level of social benefit in terms of the number of users admitted to the system.

Time-Sensitive Networking (TSN) and Deterministic Networking (DetNet) are emerging standards to enable deterministic, delay-critical communication in such networks. This naturally (re-)calls attention to the network calculus theory (NC), since a rich set of results for delay guarantee analysis have already been developed there. One could anticipate an immediate adoption of those existing network calculus results to TSN and DetNet. However, the fundamental difference between the traffic specification adopted in TSN and DetNet and those traffic models in NC makes this difficult, let alone that there is a long-standing open challenge in NC. To address them, this paper considers an arrival time function based max-plus NC traffic model. In particular, the mapping between the TSN / DetNet and the NC traffic model is proved. In addition, the superposition property of the arrival time function based NC traffic model is found and proved. Appealingly, the proved superposition property shows a clear analogy with that of a well-known counterpart traffic model in NC. These results help make an important step forward towards the development of a system theory for delay guarantee analysis of TSN / DetNet networks.

Packet loss rate (PLR) is a crucial and popular link quality metric for wireless sensor networks (WSNs). In this paper, we investigate how to estimate PLR of an IEEE 802.15.4 link from the information that is easily obtained from radio chip. Specifically, we aim to establish a generalized model that connects PLR to link quality indicator (LQI), a physical layer link quality measure, and packet length under diverse environmental conditions. To this aim, an extensive experimental study considering various environmental factors and packet lengths is conducted, from which rich observations are made on the spatio-temporal characteristics of the dependency of PLR on LQI and packet length. Based on the observations, we propose a packet loss rate model as a function of LQI and packet length, that is applicable in all experimented scenarios. Besides, a comparison with a literature LQI-only based PLR model shows that our proposed model has higher accuracy for various packet lengths. Finally, we provide the implications of the empirical study and the guidelines for real-world WSN applications to construct and adapt the proposed PLR model in different environments.

Accurate detection of the channel quality degradation is crucial for applying effective remedial actions to ensure the reliability of IEEE 802.15.4 links. Without knowing the channel quality is degraded, remedial actions may lead to more packet losses, e.g., increasing transmission power may cause even more interference. In this work, we aim to detect the channel quality degradation that turns a good link into a bad one, based on the received signal strength of radio links. The detection should be accurate and robust to diverse channel characteristics and dynamic environmental changes. To achieve this, we propose RADIUS, a lightweight approach that lays its foundation on a thresholding technique based on Bayesian decision theory and combines it with techniques for adapting to environmental changes. Extensive evaluation of RADIUS on a testbed shows that the employed Bayes thresholding technique outperforms two relevant state-of-the-art thresholding techniques by providing a higher accuracy consistently for all links across the network. Besides, RADIUS is able to keep a low error rate of detection (5.78% on average) in a 72-hour experiment, adapting to environmental changes. Furthermore, we developed an exemplary application of RADIUS to show how an existing transmission power tuning scheme can benefit from using RADIUS as an accurate and robust trigger for taking remedial actions.

Modeling and prediction of Packet Loss Rate (PLR) of wireless links using hardware information is essential for the design of higher-layer protocols in Wireless Sensor Networks. While many previous studies revealed the spatio-temporal variation of various link quality metrics, how environment impacts on the mapping between PLR and hardware indicators still remains unclear. Without a comprehensive understanding of such environmental impact, the acquired empirical PLR models are severely limited to specific scenarios. In this paper, we present the results of indoor and outdoor experimental campaigns focusing on the impact of various environmental factors (e.g., obstacles, human activities, climate conditions) on the dependency between the link PLR, signal to noise ratio (SNR) and packet length. Rich observations are made on the spatio-temporal characteristics of the PLR-SNR relationship and our analysis shows that link PLR can be modeled, in all experimented scenarios, as an exponential function of SNR and packet length with two model parameters that may vary over space and time. Besides, implications of the observations are summarized, providing guidelines to construct and adapt PLR models in different environments.

Network function virtualization (NFV) implements network middleboxes in software, enabling them to be more flexible and dynamic. NFV resource allocation methods can exploit the capabilities of virtualization to dynamically instantiate network functions (NFs) to adapt to traffic demand and network conditions. Deploying NFs requires decisions for NF placement, and routing of flows through these NFs in accordance with the sequence of NFs required to process each flow. The challenges in developing an NFV resource allocation scheme include the need to manage the dependency between flow-level (routing) and network-level (placement) decisions and to efficiently utilize resources that may be distributed network-wide, while fulfilling the performance requirements of flows. We propose a scalable resource allocation scheme, called ClusPR, that addresses these challenges. By elegantly capturing the dependency between flow routing and NF placement, ClusPR strikes a balance between multiple objectives including minimizing path stretch, balancing the load among NF instances, while maximizing the total network utilization by accommodating the maximum number of flows possible. ClusPR addresses the offline problem of NFV resource allocation. To address the online problem of dynamically placing and routing flows upon their arrival, we propose iClusPR. iClusPR is an online algorithm that performs dynamic scaling by adjusting the number of NF instances based on the traffic demand and the network state. Our experiments show that ClusPR achieves the near-optimal solution for a practical large-sized network in reasonable time. Compared to the state-of-the-art approaches, ClusPR decreases the average normalized delay by a factor of 1.2 - 1.6 × and the worst-case delay by more than 10 ×, with the same or slightly better network utilization and balances the load among NF instances. Furthermore, the performance of iClusPR, the online version, is comparable to the offline ClusPR algorithm.

Parallel computing has become a standard tool with architectures such as Google MapReduce, Hadoop, and Spark being broadly used in applications such as data processing and machine learning. Common to these systems are a fork operation, where jobs are first divided into tasks that are processed in parallel, and a join operation where completed tasks wait for the other tasks of the job before leaving the system. The synchronization constraint of the join operation makes the analysis of fork-join systems challenging, and few explicit results are known. In this work, we formulate a max-plus server model for parallel systems which allows us to derive performance bounds for a variety of systems in the GII GI and G I G cases. We contribute end-to-end delay bounds for multi-stage fork-join networks. We perform a detailed comparison of different multi-server configurations, including an analysis of single-queue fork-join systems that achieve a fundamental performance gain. We compare these results to both simulation and a live Spark system.

In a multihop wireless network, it is crucial but challenging to schedule transmissions in an efficient and fair manner. In this paper, a novel distributed node scheduling algorithm, called Local Voting, is proposed. This algorithm tries to semi-equalize the load (defined as the ratio of the queue length over the number of allocated slots) through slot reallocation based on local information exchange. The algorithm stems from the finding that the shortest delivery time or delay is obtained when the load is semi-equalized throughout the network. In addition, we prove that, with Local Voting, the network system converges asymptotically toward the optimal scheduling. Moreover, through extensive simulations, the performance of Local Voting is further investigated in comparison with several representative scheduling algorithms from the literature. Simulation results show that the proposed algorithm achieves better performance than the other distributed algorithms in terms of average delay, maximum delay, and fairness. Despite being distributed, the performance of Local Voting is also found to be very close to a centralized algorithm that is deemed to have the optimal performance.

The net neutrality principle states that users should have equal access to all Internet content and that Internet Service Providers (ISPs) should not practice differentiated treatment on any of the Internet traffic. While net neutrality aims to restrain any kind of discrimination, it also grants exemption to a certain category of traffic known as specialized services (SS), by allowing the ISP to dedicate part of the resources for the latter. In this work, we consider a heterogeneous LTE/WiFi wireless network and we investigate revenue‐maximizing Radio Access Technology (RAT) selection strategies that are net neutrality‐compliant, with exemption granted to SS traffic. Our objective is to find out how the bandwidth reservation for SS traffic would be made in a way that allows maximizing the revenue while being in compliance with net neutrality and how the choice of the ratio of reserved bandwidth would affect the revenue. The results show that reserving bandwidth for SS traffic in one RAT (LTE) can achieve higher revenue. On the other hand, when the capacity is reserved across both LTE and WiFi, higher social benefit in terms of number of admitted users can be realized, as well as lower blocking probability for the Internet access traffic.

In this paper, we investigate the delay and delay-constrained throughput performance of a point-to-point wireless-powered communication system, where one node, e.g. a user equipment (UE), is powered by the wireless energy transferred from the other node, e.g. an access point (AP), and uses the harvested wireless energy to send data to the other node. Our focus is on the delay performance of sending data over the uplink from the UE node to the AP node, and on its throughput performance when a delay constraint is enforced. Two representative time allocation schemes in using the link for the AP node to transfer energy (maybe together with data) and for the UE node to send data are considered. In particular, a lower bound on the cumulative capacity of the uplink is derived. In addition, an upper bound on the delay distribution is obtained for stochastic traffic arrivals, based on which, the delay-constrained throughput performance is further analyzed. Moreover, the accuracy of the analysis is validated by comparison with extensive simulation results. The analysis and results shed new light on the performance of such a wireless-powered communication system.

WiFi Direct is a recent device-to-device communication technology standardized by the WiFi Alliance. Its increasing availability on popular mobile systems (e.g. Android) presents a unique opportunity for developers to implement mobile social networks (MSNs), a new paradigm that facilitates data dissemination without Internet access by leveraging human mobility and short-range communication technologies. Since WiFi Direct is not originally designed for such applications, it is significant to learn its performance in practice. In this paper, we investigate goodput and fairness of WiFi Direct for data dissemination in MSNs. To this end, we develop an MSN application and conduct three sets of experiments on a testbed comprising several Android devices. Experimental results show that the data loads and mobility of nodes greatly impact the goodput and fairness.

The vast penetration of smart mobile devices provides a unique opportunity to make mobile social networking pervasive by leveraging the feature of short-range wireless communication technologies (e.g. WiFi Direct). In this paper, we study local content dissemination in WiFi-Direct-based mobile social networks (MSNs). We propose a simple GO-coordinated dissemination strategy, as WiFi Direct does not originally support content dissemination. Due to mobility and the short transmission range, the duration of nodes in contact tends to be limited and consequently they compete for the limited airtime to disseminate their own data. Therefore, fair allocation of the limited airtime among the nodes is required. We focus on fairness in content dissemination rate, which is a key application-layer metric, rather than fairness in throughput or airtime and formulate the allocation problem as a generalized Nash bargaining game wherein the nodes bargain for a share of the limited airtime. The game is proved to have a unique optimal solution, and an algorithm with low complexity is designed to find the optimal solution. Furthermore, we propose a detailed scheduling approach to implement the optimal solution. We also present numerical results to evaluate the Nash bargaining based allocation and scheduling.

In the paper a multi-agent network system of different computing nodes is considered. A problem of load balancing in the network is addressed. The problem is formulated as consensus achievement problem and solved via local voting protocol. Agents exchange information about their states in presence of noise in communication channels. At certain moment network system topology changes and new step size of control protocol is chosen to meet new conditions. Step size adjustment is done by stochastic approximation type algorithm. Analytically obtained optimal step size values are given. Simulation example demonstrating step size adjustment is provided.

Parallel systems have received increasing attention with numerous recent applications such as fork-join systems, load-balancing, and l-out-of-k redundancy. Common to these systems is a join or resequencing stage, where tasks that have finished service may have to wait for the completion of other tasks so that they leave the system in a predefined order. These synchronization constraints make the analysis of parallel systems challenging and few explicit results are known. In this work, we model parallel systems using a max-plus approach that enables us to derive statistical bounds of waiting and sojourn times. Taking advantage of max-plus system theory, we also show end-to-end delay bounds for multi-stage fork-join networks. We contribute solutions for basic G|G|1 fork-join systems, parallel systems with load-balancing, as well as general (k, l) fork-join systems with redundancy. Our results provide insights into the respective advantages of l-out-of-k redundancy vs. load-balancing.

The communication delay of train control services has a great impact on the track utilization and speed profile of high-speed trains. This paper undertakes stochastic delay analysis of train control services over a high-speed railway fading channel using stochastic network calculus. The mobility model of high-speed railway communications system is formulated as a semi-Markov process. Accordingly, the instantaneous data rate of the wireless channel is characterized by a semi-Markov modulated process, which takes into account the channel variations due to both large- and small-scale fading effects. The stochastic service curve of high-speed railway communications system is derived based on the semi-Markov modulated process. Based on the analytical approach of stochastic network calculus, the stochastic upper delay bounds of train control services are derived with both the moment generating function method and the complementary cumulative distribution function method. The analytical results of the two methods are compared and validated by simulation.

The de facto implementation of Software Defined Networking (SDN), i.e., OpenFlow, only parses L2-L4 headers, which limits the use of SDN to employ control intelligence in application layer. In this paper, we advocate content parsing to empower SDN with finer grained control ability over traffic. Specifically, we propose a scalable content parser, called COPY, to identify and parse application layer protocols. COPY creates a distinguishable counting context free grammar (DCCFG) to specify the protocol's semantics in application layer, and translates multiple DCCFGs into one distinguishable counting automaton (DCA). DCA is generated without semantic loss from the single DCCFG, and thus provides accurate and scalable parsing ability. Our experiments show that COPY precisely identifies every packet in a labeled trace. When comparing with other six approaches on the real traces, COPY performs 4.2Gb/s and 24.7Gb/s with single- and eight-thread models, respectively, which improves 20%-860% than others, and consumes acceptable offline overhead in time and space.

The combination with Orthogonal Frequency Multiple Access (OFDMA) technology is an effective approach for improving the performance of random channel access in wireless broadband networks, such as LTE, WiMax and the next-generation WLAN. In this paper, we propose a mathematical model to analyze the delay performance of an OFDMA-random-access system under unsaturated load condition. The proposed model adopts a general distribution for the duration of each time slot. In this way, the model no longer depends on a certain protocol. On the other hand, our model supports a more general setting, that is, a terminal may simultaneously access multiple subchannels in the system. Under this general assumption, we investigate the system delay performance and the stability condition. Simulation results not only show the accuracy of the presented analysis, but also demonstrate that the analysis based on saturated load assumption may underestimate the actual performance of the OFDMA-random-access system.

Many applications of wireless sensor networks (WSNs) need to balance multiple yet often conflicting performance requirements such as high energy efficiency, high throughput, low delay and low loss. Finding appropriate WSN parameter configuration to achieve the best trade-off requires in depth understanding of the joint effect of key parameters residing at different layers on the performance. In this paper, we present an extensive experimental study on the data delivery performance of aWSN link, where 4 major performance metrics, namely energy, throughput, delay and loss, were measured over 6 months under around 50 thousand parameter configurations of 7 key stack parameters. Different from existing work, rich observations are made out of the extensive measurement data, with the focus on the joint effect of these parameters on the performance. Specifically, for each of the four performance metrics, a set of guidelines is derived for parameter optimization. In addition, we propose empirical models for each performance metric to quantify the joint effects, which enable finding optimal settings for parameters such as payload size or retransmissions, in consideration of link quality and other parameter settings, to achieve better performance trade-offs. To demonstrate the potential of this work, the obtained joint parameter optimization results are applied to an example. The outcome is compared with those achieved by following representative single-parameter tuning guidelines from the literature. The comparison reveals that by considering the joint effect of multi-layer parameters together, a WSN application can obtain a much improved performance trade-off.

Duty cycling has been widely used in Wireless Sensor Networks (WSNs). While its general impact on the network performance is intuitive and clear, this understanding is far from sufficient for WSN planning, design and optimization, among others. In this paper, a quantitative study on the relationship between duty cycle and end-to-end network performance, in terms of delay and throughput, is presented, where the network diameter is also taken into consideration. Particularly, closed-form expressions are derived, revealing the impact of duty cycle on the end-to-end performance in a wireless sensor network. Numerical results illustrating the impact are presented and discussed.

This paper is devoted to the approximate consensus problem for stochastic networks of nonlinear agents with switching topology, noisy, and delayed information about agent states. A local voting protocol with nonvanishing (e.g., constant) step size is examined under time-varying environments of agents. To analyze dynamics of the closed-loop system, the so-called method of averaged models is used. It allows us to reduce analysis complexity of the closed-loop stochastic system. We derive the upper bounds for mean square distance between states of the initial stochastic system and its approximate averaged model. These upper bounds are used to obtain conditions for approximate consensus achievement. An application of general theoretical results to the load balancing problem in stochastic dynamic networks with incomplete information about the current states of agents and with changing set of communication links is considered. The conditions to achieve the optimal level of load balancing are established. The performance of the system is evaluated both analytically and by simulation.

This paper discovers fundamental principles of the backoff process that governs the performance of IEEE 802.11. A simplistic principle founded upon regular variation theory is that the backoff time has a truncated Pareto-type tail distribution with an exponent of (log y)/ log m (m is the multiplicative factor and γ is the collision probability). This reveals that the per-node backoff process is heavy-tailed in the strict sense for γ > 1/m2, and paves the way for the following unifying result. The state-of-the-art theory on the superposition of the heavy-tailed processes is applied to establish a dichotomy exhibited by the aggregate backoff process, putting emphasis on the importance of time-scales on which we view the backoff processes. While the aggregation on normal time-scales leads to a Poisson process, it is approximated by a new limiting process possessing long-range dependence (LRD) on coarse time-scales. This dichotomy turns out to be instrumental in formulating short-term fairness, extending existing formulas to arbitrary population, and to elucidate the absence of LRD in practical situations. A refined wavelet analysis is conducted to strengthen this argument.

Multi-view video (MVV) consists of multiple video streams captured simultaneously by multiple closely spaced cameras and enables users to freely change their viewpoints by playing different video streams. Those close deployed cameras will capture overlapping frames (OFs) and then transmit OFs in multiple video streams. With the viewpoint change from one stream to another, redundant OFs in the latter stream can be useless for a user. Moreover, the redundant transmission will increase with the number of users and result in severe bandwidth waste for both base stations and users. In order to reduce the redundant transmission of OFs and increase energy efficiency (EE), a new architecture Overlapping Reduced Multi-view Video Transmission (ORMVVT), which is based on User dependent Multi-view video Streaming for Multi-users (UMSM), is proposed for downlink transmission of MVV in small cell networks (SCNs). Then, a novel resource allocation model is introduced to help users achieve different data rates according to their Quality of Service (QoS) requirements. Contrarily, for the simplicity of implementation, existing schemes can only support a single reception data rate for all users in a multicast group. Further, we formulate an optimization problem to maximize the EE with the QoS constraint of services. Finally, a suboptimal QoS-aware Energy-efficient Multicast Resource Allocation scheme (QEMRA) is proposed to reduce the computational complexity. Numerical results show that the proposed low-complexity QEMRA scheme is able to get a close-to-optimal performance under general fading distributions.

In asymptotic regimes, both in time and space (network size), the derivation of network capacity results is grossly simplified by brushing aside queueing behavior in nonJackson networks. This simplifying double-limit model, however, lends itself to conservative numerical results in finite regimes. To properly account for queueing behavior beyond a simple calculus based on average rates, we advocate a system theoretic methodology for the capacity problem in finite time and space regimes. This methodology also accounts for spatial correlations arising in networks with CSMA/CA scheduling and it delivers rigorous closed-form capacity results in terms of probability distributions. Unlike numerous existing asymptotic results, subject to anecdotal practical concerns, our transient results can be used in practical settings, e.g., to compute the time scales at which multi-hop routing is more advantageous than single-hop routing.

We propose stochastic performance analysis in order to provide probabilistic quality-of-service guarantees in on-chip packet-switching networks. In contrast to deterministic analysis which gives per-flow absolute delay bound, stochastic analysis derives per-flow probabilistic delay bounding function, which can be used to avoid over-dimensioning network resources. Based on stochastic network calculus, we build a basic analytic model for an on-chip router, propose and exemplify a stochastic performance analysis flow. In experiments, we show the correctness and accuracy of our analysis, and exhibit its potential in enhancing network utilization with a relaxed delay requirement. Moreover, the benefits of such relaxation is demonstrated through a video playback application.

Carrier Aggregation (CA) has emerged as a promising technique for Long-Term Evolution Advanced (LTE-A) wireless communications to satisfy the ever-increasing bandwidth requirements. However, this technical envisagement puts forward new challenges on radio resource allocation such as serious unbalanced loads among different Component Carriers (CCs). To alleviate this problem, a novel QoS-aware resource allocation scheme, termed as Cross-CC User Migration (CUM) scheme, is proposed in this paper to support real-time services, taking into consideration the system throughput, user fairness and QoS constraints. The experiment results show that the proposed scheme outperforms the well-known Two-Level scheduling scheme in terms of packet loss probability, average queue length and throughput per user.

CHOKe is a simple and stateless active queue management (AQM) scheme. Apart from low operational overhead, a highly attractive property of CHOKe is that it can protect responsive TCP flows from unresponsive UDP flows. Particularly, previous works have proven that CHOKe is able to bound both bandwidth share and buffer share of (a possible aggregate) UDP traffic (flow) on a link. However, these studies consider, and pertain only to, a steady state where the queue reaches equilibrium in the presence of many (long-lived) TCP flows and an unresponsive UDP flow of fixed arrival rate. If the steady-state conditions are perturbed, particularly when UDP traffic rate changes over time, it is unclear whether the protection property of CHOKe still holds. Indeed, it can be examined, for example, that when UDP rate suddenly becomes 0 (i.e., flow stops), the unresponsive flow may assume close to full utilization in sub-round-trip-time (sub-RTT) scales, potentially starving out the TCP flows. To explain this apparent discrepancy, this paper investigates CHOKe queue properties in a transient regime, which is the time period of transition between two steady states of the queue, initiated when the rate of the unresponsive flow changes. Explicit expressions that characterize flow throughputs in transient regimes are derived. These results provide additional understanding of CHOKe and give some explanation on its intriguing behavior in the transient regime.

Unwanted or unusual delay changes on an Internet path can happen, though possibly rarely. In order to understand and manage the network to potentially avoid such abrupt changes, it is crucial and challenging to locate which part of the path has contributed critically to such delay disruptions so that some corresponding actions may be taken. To tackle this challenge, the present paper proposes a simple and novel approach. The proposed approach relies only on end-to-end measurements of the path. Specifically, the end-to-end measurements include end-to-end delay measurement and traceroute measurement.With the former, abrupt delay increases are flagged and a delay disruption matrix is constructed. With the latter, a routing matrix for the end-to-end path is formed. The key idea of the proposed approach is to link the routing matrix to the delay disruption matrix and make use of compressed sensing theory to infer critical hops in the routing matrix which contribute to the abrupt delay increases. To demonstrate its effectiveness, the proposed approach is applied to a real Internet path. The results are encouraging, showing that the proposed approach is able to locate the hops that have the most significant impact on or contribute the most to abrupt increases on the end-to-end delay of the measured Internet path.

Survivability of telecommunication networks has emerged as a fundamental concern for network design and operation. The network physical infrastructures are vulnerable to correlated failures arising from disastrous events. Particularly, some disasters such as natural disasters and malicious attacks may propagate across geographical areas in a short period of time. Due to such disaster propagation, network failures may occur in more than one locations. This paper investigates network survivability in the presence of disaster propagation. Specifically, it develops a model to characterize the network performance during the transient period that starts after the disaster occurrence, in the subsequent disaster propagation, and until the network fully recovers. Based on the model, network survivability analysis is exemplified for three repair strategies. The results not only are helpful in estimating quantitatively the survivability of a network (design) but also provide insights on choosing among different repair strategies.

Wireless networks are expected to support a diverse range of quality of service requirements and traffic characteristics. This paper undertakes stochastic performance analysis of a wireless finite-state Markov channel (FSMC) by using stochastic network calculus. Particularly, delay and backlog upper bounds are derived directly based on the analytical principle behind stochastic network calculus. Both the single user and multi-user cases are considered. For the multi-user case, two channel sharing methods among eligible users are studied, i.e., the even sharing and exclusive use methods. In the former, the channel service rate is evenly divided among eligible users, whereas in the latter, it is exclusively used by a user randomly selected from the eligible users. When studying the exclusive use method, the problem that the state space increases exponentially with the user number is addressed using a novel approach. The essential idea of this approach is to construct a new Markov modulation process from the channel state process. In the new process, the multi-user effect is equivalently manifested by its transition and steady-state probabilities, and the state space size remains unchanged even with the increase of the user number. This significantly reduces the complexity in computing the derived backlog and delay bounds. The presented analysis is validated through comparison between analytical and simulation results.

This paper proposes a simple and stateless active queue management (AQM) scheme, called geometric CHOKe (gCHOKe), to protect responsive flows from unresponsive ones. The proposed gCHOKe has its root in and is a generalization of the original CHOKe. It provides an extended power of flow protection, achieved by introducing an extra flow matching trial upon each successful matching of packets. Compared to the plain CHOKe, analysis and simulation show that gCHOKe can achieve over 20% improvement in the bounds of both bandwidth and buffer space used by an aggressive flow. In addition, up to 14% of the total link capacity can be saved from the unresponsive flow, allowing responsive or rate-adaptive flows to obtain a better share of resources in the router.

Performance evaluation of the 802.11 MAC protocol is classically based on the decoupling assumption, which hypothesizes that the backoff processes at different nodes are independent. This decoupling assumption results from mean field convergence and is generally true in transient regime in the asymptotic sense (when the number of wireless nodes tends to infinity), but, contrary to widespread belief, may not necessarily hold in stationary regime. The issue is often related with the existence and uniqueness of a solution to a fixed point equation; however, it was also recently shown that this condition is not sufficient; in contrast, a sufficient condition is a global stability property of the associated ordinary differential equation. In this paper, we give a simple condition that establishes the asymptotic validity of the decoupling assumption for the homogeneous case (all nodes have the same parameters). We also discuss the heterogeneous and the differentiated service cases and formulate a new ordinary differential equation. We show that the uniqueness of a solution to the associated fixed point equation is not sufficient; we exhibit one case where the fixed point equation has a unique solution but the decoupling assumption is not valid in the asymptotic sense in stationary regime.

In network quality of service provisioning, small loss probabilities are often required. Such loss probabilities are so small that they may hardly be measured on line directly. In this paper, an indirect virtual queue (VQ) approach is proposed for loss estimation. The idea of the proposed approach is to use a scaling property between the real queue and a set of virtual queues to estimate the real loss probability from the loss probabilities in the virtual queues which can be feasibly measured. The scaling property is investigated on the basis of the maximum variance asymptotic (MVA) theory. The proposed approach like other VQ based approach has low implementation complexity. In addition, the analysis and results indicate that the proposed scheme retains the high accuracy of MVA while avoiding the high computational complexity in a direct application of the original MVA based estimation approach.

The MIMO wireless channel offers a rich ground for quality of service analysis. In this work, we present a stochastic network calculus analysis of a MIMO system, operating in spatial multiplexing mode, using moment generating functions (MGF). We quantify the spatial multiplexing gain, achieved through multiple antennas, for flow level quality of service (QoS) performance. Specifically we use Gilbert-Elliot model to describe individual spatial paths between the antenna pairs and model the whole channel by an $N$-State Markov Chain, where $N$ depends upon the degrees of freedom available in the MIMO system. We derive probabilistic delay bounds for the system and show the impact of increasing the number of antennas on the delay bounds under various conditions, such as channel burstiness, signal strength and fading speed. Further we present results for multi-hop scenarios under statistical independence.

The maximum traffic arrival rate at the network for a given delay guarantee (delay constrained throughput) has been well studied for wired channels. However, few results are available for wireless channels, especially when multiple antennas are employed at the transmitter and receiver. In this work, we analyze the network delay constrained throughput of a multiple input multiple output (MIMO) wireless channel with time-varying spatial correlation. The MIMO channel is modeled via its virtual representation, where the individual spatial paths between the antenna pairs are Gilbert-Elliot channels. The whole system is then described by a K-State Markov chain, where K depends upon the degree of freedom (DOF) of the channel. We prove that the DOF based modeling is indeed accurate. Furthermore, we study the impact of the delay requirements at the network layer, violation probability and the number of antennas on the throughput under different fading speeds and signal strength.

For research on quality of experience (QoE), mean opinion scores (MOS) are widely chosen as the results of subjective tests and the ground-truth reference for further research on objective quality modeling. Furthermore, the results of objective quality modeling are used for QoE management subsequently. Therefore, the performance of QoE management process actually depends heavily on MOS. However, the rationality of MOS for QoE management is not yet technically proven in the literature. In this paper, we first prove that subject homogeneity is implicitly assumed for obtaining MOS by modeling the arithmetic averaging process from a systematic viewpoint. However, we point out that actually subjects exhibit variability in terms of quality assessment. Then we elaborate that this mismatch may results in failures if we conduct QoE management based on MOS. Finally we propose a utility-based averaging method (uMOS) which improves the performance of QoE management.

Network coding allows a network node to code the information flows before forwarding them. While it has been theoretically proved that network coding can achieve maximum network throughput, the theoretical results usually do not consider the burstiness of data traffic, delays, and the stochastic nature in information processing and transmission. There is currently no theory to systematically model and evaluate the performance of network coding, especially when node's capacity (i.e., coding and transmission) becomes stochastic. Without such a theory, the performance of network coding under various system settings is far from clear. To fill the vacancy, we develop an analytical approach by extending the stochastic network calculus theory to tackle the special difficulties in the evaluation of network coding. We prove the new properties of the stochastic network calculus and design an algorithm to obtain the performance bounds for acyclic stochastic networks with network coding. The tightness of theoretical bounds is validated with simulation.

In the Internet, premium services require to keep a very small delay tail probability with respect to some delay threshold. Such a small probability is difficult to measure in real time due to the large measurement variability. Practical approaches measure the traffic statistics instead and derive the tail probability by queueing analysis. In this paper, we show that an accurate estimator based on in-depth queueing analysis can amplify the measurement errors and cause robustness problem. A simple and less-accurate estimator is proposed to achieve much better robustness while still keeping a satisfying accuracy. The balance between the accuracy and robustness is thus not only an issue of parameter configuration for an estimator, but also a crucial point in designing the method for online estimation. This insight illuminates a potential new way for the development of online tail probability estimators.

In this paper we present results from an extensive measurement study of various hardware and (virtualized) software routers using several queueing strategies, i.e. First-Come-First-Served and Fair Queueing. In addition to well-known metrics such as packet forwarding performance, per packet processing time, and jitter, we apply network calculus models for performance analysis. This includes the Guaranteed Rate model for Integrated Services as well as the Packet Scale Rate Guarantee model for Differentiated Services. Using a measurement approach that provides a means to estimate rate and error term of a real node, we propose an interpretation of router performance based on these parameters taking packet queueing and scheduling into account. Such estimated parameters should be used to make the analysis of real networks more accurate. We underpin the applicability of this approach by comparing analytical results of concatenated routers to real world measurements.

Stochastic network calculus requires special care in the search of proper stochastic traffic arrival models and stochastic service models. Tradeoff must be considered between the feasibility for the analysis of performance bounds, the usefulness of performance bounds, and the ease of their numerical calculation. In theory, transform between different traffic arrival models and transform between different service models are possible. Nevertheless, the impact of the model transform on performance bounds has not been thoroughly investigated. This paper is to investigate the effect of the model transform and to provide practical guidance in the model selection in stochastic network calculus.

Stochastic network calculus is an evolving theory for network performance guarantee analysis. Although many theoretical results of this theory have been developed, there still lack applicable examples to demonstrate how it may be used. This paper exemplifies applying stochastic network calculus to delay analysis of the IEEE 802.11 distributed coordination function (DCF). Analyzing and obtaining the stochastic characteristics of the single packet service time is the primary task. Then the stochastic behavior of the DCF is characterized by a time-domain server model, which describes the cumulative service time provided to an arrival flow using a probabilistic bound. Based on this server model, we obtain delay bounds for different arrival processes. In addition, the delay bounds also take buffer size into account. The analytical bounds are further discussed using numerical results. Through these, we present a stochastic network calculus approach to delay evaluation of the DCF.

Based on whether the competing active nodes are forwarding packets of the same flow, the channel access contention in multi-hop wireless networks can be classified into intra-flow contention and inter-flow contention. In this paper, we analyze the effect of intra-flow contention on multi-hop network performance. First, we demonstrate that intra-flow contention makes up a large proportion of contention in the network and may severely degrade the network performance. Then, to understand the performance improvement by eliminating intra-flow contention, we consider an ideal MAC based on 802.11 DCF where intra-flow contention is eliminated. We analyze the performance of the ideal MAC by proposing a three-dimensional Markov model. Furthermore, we present an approximate implementation of the ideal MAC based on simple modifications to 802.11 DCF. Finally, extensive simulations are performed and the results validate the proposed model and reveal the performance improvement by eliminating intra-flow contention.

A challenging research issue of analyzing networks where packets are served probabilistically, such as multi-access networks and wireless networks, is to characterize the stochastic nature of service provided to users. This paper proposes a server model for characterizing a service process with the consideration of the stochastic nature of service. The proposed server model is defined based on a probabilistic bound on the cumulative packet service time. In addition, two traffic models characterizing the arrival process by the cumulative packet inter-arrival time are defined. Based on the proposed models, stochastic service guarantees including delay bound and backlog bound and the output characterization are explored. A two-class Probabilistic Priority server system, a generic server model which can model systems serving packets probabilistically, is analyzed to illustrate that the proposed models can facilitate performance analysis.

CDMA2000, WCDMA and WiMAX are three widely used 3G technologies. Since they share the same goal, which is to provide broader coverage and higher throughput in 3G networks, an impartial comparison of their performance is indispensable. However, they are based on different design principles and methodologies, which make the comparison challenging. This paper presents a comparative study of these technologies, with focus on system spectral efficiency and stability in 3G networks. Specifically, the paper presents a framework for the comparison based on the common set of configurations adopted by these technologies, which include channel models, system parameters and key algorithms. Through extensive simulations, the system spectral efficiency and stability of CDMA2000 1x EV-DO Rev.A, WCDMA HSDPA/HSUPA and mobile WiMAX are compared. It is found that while WiMAX can provide highest throughput, the two CDMA-based technologies achieve higher system spectral efficiency, especially on the downlink. Regarding system stability, it is observed that CDMA2000 1x EV-DO Rev.A can operate under higher interference levels than WCDMA HSDPA/HSUPA and mobile WiMAX. In addition, the comparison on system spectral efficiency between CDMA2000, WCDMA and WiMAX is also conducted when relevant enhanced technologies, i.e., MIMO and interference cancellation, are adopted. We believe that our work will serve as a cornerstone for a fair comparison between the various technologies for prospective 3G networks.

Information-driven networks include a large category of networking systems, where network nodes are aware of information delivered and thus can not only forward data packets but may also perform information processing. In many situations, the quality of service (QoS) in information-driven networks is provisioned with the redundancy in information. Traditional performance models generally adopt evaluation measures suitable for packet-oriented service guarantee, such as packet delay, throughput, and packet loss rate. These performance measures, however, do not align well with the actual need of information-driven networks. New performance measures and models for information-driven networks, despite their importance, have been mainly blank, largely because information processing is clearly application dependent and cannot be easily captured within a generic framework. To fill the vacancy, we develop a new performance evaluation framework particularly tailored for information-driven networks, based on the recent development of stochastic network calculus. Particularly, our model captures the information processing and the QoS guarantee with respect to stochastic information delivery rates, which have never been formally modeled before. This analytical model is very useful in deriving theoretical performance bounds for a large body of systems where QoS is stochastically guaranteed with a certain level of information delivery.

Stochastic network calculus is a theory for stochastic service guarantee analysis of computer communication networks. In the current stochastic network calculus literature, its traffic and server models are typically defined based on the cumulative amount of traffic and cumulative amount of service respectively. However, there are network scenarios where the applicability of such models is limited, and hence new ways of modeling traffic and service are needed to address this limitation. This paper presents time-domain models and results for stochastic network calculus. Particularly, we define traffic models, which are defined based on probabilistic lower-bounds on cumulative packet inter-arrival time, and server models, which are defined based on probabilistic upper-bounds on cumulative packet service time. In addition, examples demonstrating the use of the proposed time-domain models are provided. On the basis of the proposed models, the five basic properties of stochastic network calculus are also proved, which implies broad applicability of the proposed time-domain approach.

The IEEE 802.16 is the standard for broadband wireless access. One promise of this technology (also called WiMAX) is to provide high-speed access to the Internet where the transmission control protocol (TCP) is the core transport protocol. In this paper, we study the impact of network asymmetry in WiMAX on TCP performance. In particular, we investigate the dependence of the aggregate throughput and goodput of TCP on factors such as frame duration, direction of flow, DL:UL ratio, modulation and coding schemes, and offered loads. We find that these factors affect TCP performance by exacerbating the network asymmetry inherent to the MAC layer.

Measurement based admission control (MBAC) has long been recognized as an important method for providing stochastic service guarantees. The main work in the literature has mostly been on finding optimal MBAC algorithms that maximize network utilization. On the other hand, a critical issue for MBAC, which is the robustness of an MBAC algorithm in meeting the required quality of service (QoS) under changing traffic conditions, has long been overlooked and few results are available. In this paper, we investigate the robustness of MBAC in meeting the QoS target. Specifically we evaluate three representative MBAC algorithms and show their weaknesses and strengths through comparisons with an ideal admission controller.

Network calculus is a recently developed theory dealing with queuing systems found in computer networks with focus on service guarantee analysis. In the current network calculus literature, the behavior of a server is typically modeled with the cumulative amount of service it successfully delivers, and the successfulness of service delivery implies no error in the delivered service. However, there are many networks such as wireless networks, where, not only is the service error-prone due to multi-access contention and/or random error on the communication link, but different error handling methods may also be applied. In such cases, it is difficult to directly apply the existing network calculus results due to lack of server models taking error into account. In this paper, an error server model is proposed for stochastic network calculus, based on which, an analysis on error servers is performed. The corresponding concatenation property is derived, which shows that under some general conditions, the tandem of error servers can be treated as an equivalent error server. In addition, to demonstrate the use and implication of the proposed error server model, performance bounds are derived and compared for a simple network. The study of the simple network shows that error handling may have significant impact on the performance bounds, and the proposed error server model can facilitate the analysis.

Mobile WiMAX has become one of the 3 Generation communication systems and its performance has been widely evaluated. The physical overhead is a critical factor that may affect the overall performance significantly. But almost no attention has been paid to the impact of overhead on system performance yet. In this paper, we first analyze main signaling resources needed in physical layer, which consist of the physical overhead. Then the dynamic overhead model for downlink and uplink are proposed respectively and simplified, while maintaining the simulation accuracy. Average overhead amount is obtained through system level simulation using dynamic overhead calculation. Finally, it is proved that the model is reasonable and the average overhead size may be used in stead of dynamic calculation for the sake of reducing simulation complexity as well as keeping evaluation results precise.

A basic calculus is presented for stochastic service guarantee analysis in communication networks. Central to the calculus are two definitions, maximum-(virtual)-backlog-centric (m. b. c) stochastic arrival curve and stochastic service curve, which respectively generalize arrival curve and service curve in the deterministic network calculus framework. With m. b. c stochastic arrival curve and stochastic service curve, various basic results are derived under the (min, +)algebra for the general case analysis, which are crucial to the development of stochastic network calculus. These results include (i)superposition of flows, (ii)concatenation of servers, (iii) output characterization, (iv)per-flow service under aggregation, and (v)stochastic backlog and delay guarantees. In addition, to perform independent case analysis, stochastic strict server is defined, which uses an ideal service process and an impairment process to characterize a server. The concept of stochastic strict server not only allows us to improve the basic results (i)-(v)under the independent case, but also provides a convenient way to find the stochastic service curve of a serve. Moreover, an approach is introduced to find the m.b.c stochastic arrival curve of a flow and the stochastic service curve of a server.

Long range dependent (LRD) traffic whose single server queue process is Weibull Bounded (WB) is first analyzed. Two upper bounds on the individual session's queue length of LRD traffic under the generalized processor sharing (GPS) scheduling discipline are then contributed. It is shown that the index parameter in the upper bound of one LRD flow, (in addition to the decay rate and the asymptotic constant), may be affected by other LRD flows. A new concept, called LRD isolation, is subsequently contributed and accompanying it, a new technique is contributed to check whether a flow, with a given GPS weight assignment, can be guaranteed to be LRD isolated. This technique is also amenable for use in an online call admission control (CAC) scenario. When existing flows have already been assigned contract weights that cannot be changed, our technique can be used to determine minimum contract weights to be assigned to a new flow in order to guarantee the flow to be LRD isolated. The results are also extended to a PGPS (packet-based GPS) scheduler and relevant numerical results are provided to show the usefulness of our bounds and LRD isolation technique.

Packet scale rate guarantee (PSRG) is a service guarantee defined recently for expedited forwarding (EF) service under the differentiated services framework. In the original work of PSRG [J.C.R. Bennett et al., August 2002], two classes of schedulers are proved to be PSRG servers. In addition, it is proved that end-to-end PSRG and consequently end-to-end delay bound are provided by a network of PSRG servers implementing per-flow scheduling. Moreover, a delay bound is presented for a network of PSRG servers implementing aggregate scheduling. In this paper, we show that these results can be improved for networks of PSRG servers of these schedulers. In particular, we show that the two classes of schedulers belong to a common scheduler family. In addition, we prove that the end-to-end delay bounds can be improved for networks of such schedulers. We also prove that PSRG can be derived from the delay bound and call this PSRG-from-delay-bound property. Moreover, with this property, we derive and discuss end-to-end PSRG for both the per-flow scheduling network and the aggregate scheduling network.

Frequent changes in network topologies caused by mobility in mobile ad hoc networks (MANETs) impose great challenges to designing routing schemes for such networks. Various routing schemes each aiming at particular type of MANET (e.g., flat or clustered MANETs) with different mobility degrees (e.g., low, medium, and high mobility) have been proposed in the literature. However, since a mobile node should not be limited to operate in a particular MANET assumed by a routing scheme, an important issue is how to enable a mobile node to achieve routing performance as high as possible when it roams across different types of MANETs. To handle this issue, a quantity that can predict the link status for a time period in the future with the consideration of mobility is required. In this paper, we discuss such a quantity and investigate how well this quantity can be used by the link caching scheme in the dynamic source routing protocol to provide the adaptability to variable topologies caused by mobility through computer simulation in NS-2.

In this paper, we propose a probabilistic preemptive (PP) scheme for service differentiation in optical burst switching (OBS) networks. By changing the preemptive probability, an OBS node can adjust the ratio of burst blocking probability between different traffic classes, while the overall blocking probability is not affected.

With on-demand routing protocols for mobile ad hoc networks (MANETs), for example, dynamic source routing (DSR), a source node tries to find a route to the destination node only when it has a packet addressed to that destination node. And the source node can find a route either from its own route cache or by initializing a route discovery. To reduce the traffic generated by such route discoveries, caching of routing information is necessary. And in the design of caching schemes, cache timeout (i.e., the time period that a link would stay in the cache) is a critical parameter. However, only a few works have been done on how to set it. In this paper, we propose a new link caching scheme with adaptive timeouts. In this scheme, the cache timeout of a link is decided based on the estimation of the link's lifetime. Simulation results show that the proposed scheme can adapt to scenario changes and hence improve the performance of on-demand routing for MANETs.

To analyze scheduling algorithms in high-speed packet switching networks, two models have been proposed and widely used in the literature, namely the guaranteed rate (GR) server model and the latency rate (LR) server mode. While a lot scheduling algorithms have been proved to belong to GR or LR or both, it is not clear what is the relationship between them. The purpose of this paper is to investigate this relationship. We prove that if a scheduling algorithm that belongs to GR, it also belongs to LR and vice versa. In addition, we review several other models that are also used for analysis of scheduling algorithms and discuss their relationship with LR model and GR model.

To support quality of service guarantees in a scalable manner, aggregate scheduling has attracted a lot of attention in the networking community. However, while there are a large number of results available for flow-based scheduling algorithms, few such results are available for aggregate-based scheduling. We study a network implementing guaranteed rate (GR) scheduling with first-in-first-out (FIFO) aggregation. We derive an upper bound on the worst case end-to-end delay for the network. We show that while for a specific network configuration, the derived delay bound is not restricted by the utilization level on the guaranteed rate, it is so for a general network configuration.

With the development of high-speed backbone networks, more and more traffic load is pushed to the Internet edge equipment and end hosts. Newly emerged bottleneck problems in end systems ask for quality of service (QoS) to be deployed in them. Meanwhile, the tremendous traffic brought by multimedia communications asks for end-to-end QoS. Facing these facts and challenges, a framework of deploying QoS in end systems is presented. The framework aims at both relieving bottleneck problems through utilizing limited resources efficiently and guaranteeing end-to-end QoS by integrating network QoS and end system QoS. In addition, the framework combines the functions of managing both network QoS and end system QoS.

We introduce the concept of generalized stochastically bounded burstiness (gSBB) for Internet traffic, the tail distribution of whose burstiness can be bounded by a decreasing function in a function class with few restrictions. This new concept extends the concept of stochastically bounded burstiness (SBB) introduced by previous researchers to a much larger extent - while the SBB model can apply to Gaussian self-similar input processes, such as fractional Brownian motion, gSBB traffic contains non-Gaussian self-similar input processes, such as /spl alpha/-stable self-similar processes, which are not SBB in general. We develop a network calculus for gSBB traffic. We characterize gSBB traffic by the distribution of its queue size. We explore the property of sums of gSBB traffic and the relation of input and output processes. We apply this calculus to a work-conserving system shared by a number of gSBB sources, to analyze the behavior of output traffic for each source and to estimate the probabilistic bounds for delays. We expect this new calculus to be of particular interest in the implementation of services with statistical qualitative guarantees.

We analyze the arrival process of long range dependent (LRD) traffic and demonstrate that it is a Weibull bounded burstiness (WBB) process. By decomposing a generalized processor sharing (GPS) system into isolated queues and servers, we then obtain two upper bounds on the individual session queue length, which are useful for quality of service (QoS) control. We also demonstrate that some parameters in determining the upper bound on an individual session queue, such as the index, the asymptotic constant and the decay rate may be affected by other flows existing in the GPS system. However, under certain conditions, by carefully choosing the GPS weight parameters, an individual session with LRD traffic input can be well isolated from other flows.

This paper proposes a novel scheduling discipline for service differentiation in multi-service networks, which is referred to as the probabilistic priority (PP) discipline. The PP is based on the strict priority (SP) discipline with the difference that each priority class is assigned a parameter. The parameter determines the probability with which its corresponding queue is served when it is polled by the server. Service differentiation as well as fairness among traffic classes can be achieved in PP by setting the assigned parameters properly. In addition, PP can be easily reduced to the ordinary SP or to the reverse SP. Moreover, PP can provide service segregation among groups of traffic classes while at the same time provide service differentiation among classes within each group.

Future integrated services networks will need to provide quality of service (QoS) guarantees to multimedia applications. To ensure that the contracted QoS is sustained, it is not sufficient to just commit resources. QoS monitoring is required to detect and locate the degradation of QoS performance. In addition, the distribution of QoS, instead of simply end-to-end QoS, needs to be monitored. In QoS distribution monitoring, the distribution of QoS experienced by a real-time flow in different network segments is monitored. This paper presents a brief survey of current QoS monitoring-related mechanisms, followed by a discussion of the challenges involved in providing QoS distribution monitoring. Several approaches are then proposed to meet these challenges.